Dataset Information

Imaging Flow Cytometry Quantifies Neural Genome Dynamics.

ABSTRACT: Somatic mosaicism is a common consequence of normal development. DNA repair is simply not perfect, and each cell's genome incurs continuous DNA damage as a consequence of transcription, replication, and other cell biological stressors. Brain somatic mosaicism is particularly noteworthy because the vast majority of an individual's neurons are with that individual for life and neural circuits give rise directly to behavioral phenotypes. Brain somatic mosaicism, now revealed and tractable due to advances in single cell 'omic approaches, has emerged as an intriguing and unexplored aspect of neuronal diversity. Furthermore, the study of DNA damage during early neurodevelopment, when the rate of mutagenesis is high, is the perfect starting point to understand the origins of brain mosaicism. Flow cytometry is a highly efficient technique to study cell cycle and intracellular proteins of interest, particularly those related to DNA damage, but it lacks the high resolution of microscopy to examine the localization of these proteins. In this study, we outline a novel single-cell approach to quantify DNA double-strand break (DNA DSB) dynamics during early human neurodevelopment by applying imaging flow cytometry (IFC) to human-induced pluripotent stem cell-derived neural progenitor cells (NPCs) undergoing neurogenesis. We establish an increase of DNA DSBs by quantifying ?H2AX foci in mildly stressed NPCs using various single-cell approaches in addition to IFC including fluorescent microscopy, conventional flow cytometry, and measuring DNA DSBs with the comet assay. We demonstrate the dose-dependent sensitive detection of ?H2AX foci through IFC and reveal the dynamics of DNA DSBs in proliferating and differentiating neural cells in early neurogenesis. © 2019 International Society for Advancement of Cytometry.

SUBMITTER: Michel N

PROVIDER: S-EPMC7851630 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Imaging Flow Cytometry Quantifies Neural Genome Dynamics.

Michel Nadine N Majumdar Usnish B UB Lannigan Joanne J McConnell Michael J MJ

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20190507 8

Somatic mosaicism is a common consequence of normal development. DNA repair is simply not perfect, and each cell's genome incurs continuous DNA damage as a consequence of transcription, replication, and other cell biological stressors. Brain somatic mosaicism is particularly noteworthy because the vast majority of an individual's neurons are with that individual for life and neural circuits give rise directly to behavioral phenotypes. Brain somatic mosaicism, now revealed and tractable due to ad ...[more]

PMID: 31063256

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Project description:BackgroundUpon platelet activation, a subpopulation of procoagulant platelets is formed, characterized by the exposure of the anionic aminophospholipid phosphatidylserine (PS) on the surface membrane.ObjectiveTo evaluate procoagulant PS-exposing platelets by imaging flow cytometry.MethodsPlatelet ultrastructure was examined by transmission electron microscopy, and a comprehensive analysis of procoagulant platelets was performed using imaging flow cytometry; platelets were fluorescently labeled for the markers glycoprotein (GP)IX, activated integrin αIIbβ3, CD62P, and PS exposure.ResultsA subpopulation of platelets stimulated in suspension by the physiological agonists thrombin+collagen, and all platelets stimulated by the calcium ionophore A23187, had a distinct round morphology. These platelets were PS-exposing, larger in size, had an increased circularity index, and had reduced internal complexity compared with non-PS-exposing platelets. They expressed CD62P and αIIbβ3 in an inactive conformation on the surface, and demonstrated depolarized inner mitochondrial membranes. For the first time, using imaging flow cytometry, a large proportion of PS-exposing platelets possessing platelet-associated extracellular vesicles (EVs) was observed, which demonstrated heterogeneous platelet marker expression that was different from free released EVs.ConclusionsInnovative imaging flow cytometry allowed detailed fluorescence-based, quantitative morphometric analysis of PS-exposing platelets; in becoming procoagulant, platelets undergo remarkable morphological changes, transforming into spherical "balloons," almost devoid of their normal internal architecture. Almost all PS-exposing platelets have associated EVs that are not detectable by traditional flow cytometry. While their functions have yet to be fully elucidated, the heterogeneity of platelet-associated and released EVs suggests that they may contribute to different aspects of hemostasis and of thrombosis.

Project description:Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

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Imaging Flow Cytometry Quantifies Neural Genome Dynamics.

Publications

Imaging Flow Cytometry Quantifies Neural Genome Dynamics.

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